独立采样比智能推理更省算力且更高效
When Independent Sampling Outperforms Agentic Reasoning
- 用多次独立采样替代复杂推理流程
- 在216道题上准确率更高，调用次数更少
- 适合资源有限的算法竞赛场景
我们在固定推理预算下研究编程竞赛中的算力分配策略。在涵盖一至三等级的216道Codeforces题目上，对比了基于代理的推理与重复独立采样（k-shot）方法在成本与调用次数上的表现。无论模型或难度如何，k-shot始终在准确率-成本与准确率-查询数之间取得更优权衡。即使在代理框架中使用提示缓存，其单次调用效率仍较低。结果表明，在自包含的算法任务中，受现实资源约束时，独立探索优于深度代理推理。我们还分析了固定预算下的分配策略，并证明最优解可最小化单位美元的对数失败概率。
原文摘要 · Abstract (English)
We study how to allocate inference-time compute for competitive programming under fixed budgets. Evaluating 216 Codeforces problems across Divisions 1-3, we compare agent-based reasoning with repeated independent sampling (k-shot) as a function of both cost and number of model calls. Across models and difficulty levels, k-shot consistently achieves a better accuracy-cost and accuracy-query tradeoff. This gap persists despite prompt caching in agent frameworks, indicating lower per-call effectiveness. Our results show that, for self-contained algorithmic tasks, independent exploration can outperform deeper agentic reasoning under realistic resource constraints. We also provide a budget-allocation analysis when the inference budget is fixed, and prove that a cost-optimal solver minimizes the principled metric log failure likelihood per dollar.
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